
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from sklearn.linear_model import LinearRegression
from scipy.cluster.hierarchy import dendrogram, linkage, cophenet
from scipy.spatial.distance import pdist
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_score




%matplotlib inline 
import warnings
warnings.filterwarnings("ignore")


# Function to create barplots that indicates percentage for each category
def bar_perc(data, column):
    
    total = len(data[column]) # Length of the column
    plt.figure(figsize = (15, 5))
    
    # plt.xticks(rotation = 45) 
    ax = sns.countplot(data[column], palette = 'Paired')
    
    for p in ax.patches:
        percentage = '{:.1f}%'.format(100 * p.get_height() / total) # Percentage of each class
        x = p.get_x() + p.get_width() / 2 - 0.05                    # Width of the plot
        y = p.get_y() + p.get_height()                              # Height of the plot
        ax.annotate(percentage, (x, y), size = 12)                  # Annotate the percentage 
        
    plt.show()


# This function takes the numerical variable as the input and returns the boxplots and histograms for that variable
# This would help us write faster and cleaner code

def histogram_boxplot(feature, figsize = (15,10), bins = None):
    """ Boxplot and histogram combined
    feature: 1-d feature array
    figsize: size of fig (default (9,8))
    bins: number of bins (default None / auto)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(nrows = 2,     # Number of rows of the subplot grid
                                           sharex = True, # The X-axis will be shared among all the subplots
                                           gridspec_kw = {"height_ratios": (.25, .75)}, 
                                           figsize = figsize 
                                           ) 
    # Creating the subplots
    # Boxplot will be created and the mean value of the column will be indicated using some symbol
    sns.boxplot(feature, ax = ax_box2, showmeans = True, color ='red')
    
    # For histogram
    sns.histplot(feature, kde = True, ax = ax_hist2, bins = bins) if bins else sns.histplot(feature, kde = True, ax = ax_hist2)
    ax_hist2.axvline(np.mean(feature), color = 'g', linestyle = '--')      # Add mean to the histogram
    ax_hist2.axvline(np.median(feature), color = 'black', linestyle = '-') # Add median to the histogram
    
    # Calculate the boundaries of outliers
    iqr = np.percentile(feature, 75) - np.percentile(feature, 25)
    lower_bound = np.percentile(feature, 25) - 1.5 * iqr
    upper_bound = np.percentile(feature, 75) + 1.5 * iqr
    
    # Get the minimum and maximum values (boundaries of outliers)
    minimum = np.min(feature[feature >= lower_bound])
    maximum = np.max(feature[feature <= upper_bound])
    
    print('lower_bound: ', minimum)
    print('upper_bound', maximum)


#__________________
# read the datafile

pd.set_option('display.max_columns', None)


df_initial = pd.read_csv('marketing_campaign+%284%29.csv')
print('Dataframe dimensions:', df_initial.shape)
if df_initial.duplicated().any():
    print('hasDuplicates:', df_initial.duplicated().sum())
else:
    print('hasDuplicates:', False)
#____________________________________________________________
# gives some infos on columns types and numer of null values
tab_info = pd.concat([pd.DataFrame(df_initial.dtypes).T.rename(index={0:'column type'}), 
                      pd.DataFrame(df_initial.isnull().sum()).T.rename(index={0:'null values (nb)'}), 
                      pd.DataFrame(df_initial.isnull().sum()/df_initial.shape[0]*100).T.rename(index={0:'null values (%)'})])

display(tab_info)

#__________________
# show first lines
display(df_initial[:5])

Dataframe dimensions: (2240, 27)
hasDuplicates: False


df_initial.describe()


# To remove the NA values
df = df_initial.dropna()

# Number of columns that contain null values in df_initial
num_null_columns = df_initial.isnull().any().sum()


num_rows_dropped = len(df_initial) - len(df)
num_remaining_rows = len(df)

# Determine pluralization for columns
column_plural = "column" if num_null_columns == 1 else "columns"

# Print the results
print(f'Dropped {num_rows_dropped} rows from {num_null_columns} {column_plural}. The total remaining rows are {num_remaining_rows:,}.')

Dropped 24 rows from 1 column. The total remaining rows are 2,216.


# Convert Dt_Customer column to datetime
df_initial['Dt_Customer'] = pd.to_datetime(df_initial['Dt_Customer'], format='%d-%m-%Y', errors='coerce')


df_initial[df_initial.isnull().any(axis=1)]


def describe_dataframe(df, include_number=True):
    if include_number:
        df_desc = df.describe(include='number').T
    else:
        df_desc = df.describe(exclude='number').T

    df_col = df_desc.index.tolist()

    for column in df_col:
        print(df[column].value_counts())
        print('-' * 50)
        
df.describe(exclude = 'number').T


# Replace 2n Cycle for Master
df.loc[df['Education'] == '2n Cycle', 'Education'] = 'Master'

# Grouped small value counts as Other
df.loc[df['Marital_Status'].isin(['Alone', 'Absurd', 'YOLO']), 'Marital_Status'] = 'Single'


df.describe(include = 'number').T


cat_cols = df.describe(exclude='number').T.index.tolist()
num_cols = df.describe(include='number').T.index.tolist()


cat_cols

['Education', 'Marital_Status', 'Dt_Customer']


bar_perc(df, 'Education')


bar_perc(df, 'Kidhome')


bar_perc(df, 'Teenhome')


bar_perc(df, 'Complain')


bar_perc(df, 'Marital_Status')


num_cols

['ID',
 'Year_Birth',
 'Income',
 'Kidhome',
 'Teenhome',
 'Recency',
 'MntWines',
 'MntFruits',
 'MntMeatProducts',
 'MntFishProducts',
 'MntSweetProducts',
 'MntGoldProds',
 'NumDealsPurchases',
 'NumWebPurchases',
 'NumCatalogPurchases',
 'NumStorePurchases',
 'NumWebVisitsMonth',
 'AcceptedCmp3',
 'AcceptedCmp4',
 'AcceptedCmp5',
 'AcceptedCmp1',
 'AcceptedCmp2',
 'Complain',
 'Response']


histogram_boxplot(df.Income, figsize = (15,10), bins = None)

lower_bound:  1730.0
upper_bound 113734.0


df = df[df['Income'] < 200000]


histogram_boxplot(df.Income, figsize = (15,10), bins = None)

lower_bound:  1730.0
upper_bound 113734.0


histogram_boxplot(df.Year_Birth, figsize = (15,10), bins = None)

lower_bound:  1940
upper_bound 1996


histogram_boxplot(df.NumWebPurchases, figsize = (15,10), bins = None)

lower_bound:  0
upper_bound 11


histogram_boxplot(df.Year_Birth, figsize = (15,10), bins = None)

lower_bound:  1940
upper_bound 1996


histogram_boxplot(df.NumCatalogPurchases, figsize = (15,10), bins = None)

lower_bound:  0
upper_bound 10


histogram_boxplot(df.NumStorePurchases, figsize = (15,10), bins = None)

lower_bound:  0
upper_bound 13


histogram_boxplot(df.NumWebVisitsMonth, figsize = (15,10), bins = None)

lower_bound:  0
upper_bound 13


# describe_dataframe(df, include_number=False)


plt.figure(figsize = (16, 16))

sns.heatmap(df.corr(), annot = True, fmt = '0.2f') # Shows correlation between variables.

plt.show()


# Relationship between food_preparation_time and cuisine type
plt.figure(figsize=(15,7))
sns.boxplot(x = "Education", y = "Income", data = df, palette = 'PuBu')
plt.xticks(rotation = 60)
plt.show()


# Relationship between food_preparation_time and cuisine type
plt.figure(figsize=(15,7))
sns.boxplot(x = "Marital_Status", y = "Income", data = df, palette = 'PuBu')
plt.xticks(rotation = 60)
plt.show()


df["Age"] = 2016 - df.Year_Birth


df.drop(df[df["Age"] > 115].index, inplace=True)


histogram_boxplot(df.Age, figsize = (15,10), bins = None)

lower_bound:  20
upper_bound 76


df["Kids"] = df.Teenhome + df.Kidhome


df.Marital_Status.unique().tolist()

['Single', 'Together', 'Married', 'Divorced', 'Widow']


df["Marital_Status"].replace(["Married", "Together"], "Relationship", inplace=True)
df["Marital_Status"].replace(["Divorced", "Widow"], "Single", inplace=True)


df.Marital_Status.value_counts()

Relationship    1428
Single           784
Name: Marital_Status, dtype: int64


df["Status"] = df["Marital_Status"].replace({"Single": 1, "Relationship": 2})


df["Family_Size"] = df["Status"] + df["Kids"]


df["Expenses"] = df[['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']].sum(axis=1)


df["NumTotalPurchases"] = df[['NumDealsPurchases', 'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases']].sum(axis=1)


df["Dt_Customer"] = pd.to_datetime(df["Dt_Customer"], format="%d-%m-%Y")


df["Dt_Customer"].min()

Timestamp('2012-07-30 00:00:00')


df["Dt_Customer"].max()

Timestamp('2014-06-29 00:00:00')


 # Assigning date to the day variable
df["day"] = "01-01-2015"                         

# Converting the variable day to Python datetime object
df["day"] = pd.to_datetime(df.day)


df["Engaged_in_days"] = (df["day"] - df["Dt_Customer"]).dt.days


df["TotalAcceptedCmp"] = df[["AcceptedCmp1", "AcceptedCmp2", "AcceptedCmp3", "AcceptedCmp4", "AcceptedCmp5", "Response"]].sum(axis=1)


df['AmountPerPurchase'] = df['Expenses'] / df['NumTotalPurchases']


df['AmountPerPurchase'].max()

inf


df[df['NumTotalPurchases'] == 0].shape[0]

4


df.drop(df[df['NumTotalPurchases'] == 0].index, inplace=True)


histogram_boxplot(df.AmountPerPurchase, figsize = (15,10), bins = None)

lower_bound:  0.5333333333333333
upper_bound 98.91666666666667


# Dropping all the irrelevant columns and storing in data_model
data_model = df.drop(
    columns=[
        "ID",
        "Year_Birth",
        "Dt_Customer",
        "day",
        "Complain",
        "Response",
        "AcceptedCmp1",
        "AcceptedCmp2",
        "AcceptedCmp3",
        "AcceptedCmp4",
        "AcceptedCmp5",
        "Marital_Status",
        "Status",
        "Kids",
        'Education',
        'Kidhome',
        'Teenhome', 'Income','Age', 'Family_Size'
    ],
    axis=1,
)


df.shape

(2208, 37)


data_model.shape

(2208, 17)


data_model.head()


from sklearn.preprocessing import StandardScaler


# Scaling the data and storing the output as a new DataFrame
scaler = StandardScaler()
numerical_columns = df.select_dtypes(include=[np.number])

data_scaled_all = pd.DataFrame(scaler.fit_transform(numerical_columns), columns = numerical_columns.columns)

data_scaled_all.head()


# Scaling the data and storing the output as a new DataFrame

scaler = StandardScaler()

data_scaled = pd.DataFrame(scaler.fit_transform(data_model), columns = data_model.columns)

data_scaled.head()


from sklearn.manifold import TSNE

#  t-SNE is a non-linear dimensionality reduction technique that focuses on preserving the local structure of the data
# Apply the t-SNE algorithm with random_state = 1
tsne = TSNE(n_components = 2, random_state = 1)
# Fit and transform t-SNE function on the scaled data
data_tsne_all = tsne.fit_transform(data_scaled_all) # Complete the code   

data_tsne_all = pd.DataFrame(data = data_tsne_all, columns = ['Component 1', 'Component 2'])


mkt_tsne_all = tsne.fit_transform(data_scaled_all)

# Converting the embeddings to a dataframe
mkt_tsne_all = pd.DataFrame(mkt_tsne_all, columns = ["Component 1", "Component 2"])

# Scatter plot for two components
plt.figure(figsize = (7, 7))

sns.scatterplot(x = data_tsne_all.iloc[:,0], y = data_tsne_all.iloc[:,1])

plt.show()


from sklearn.manifold import TSNE

#  t-SNE is a non-linear dimensionality reduction technique that focuses on preserving the local structure of the data
# Apply the t-SNE algorithm with random_state = 1
tsne = TSNE(n_components = 2, random_state = 1)
# Fit and transform t-SNE function on the scaled data
data_tsne = tsne.fit_transform(data_scaled) # Complete the code   

data_tsne = pd.DataFrame(data = data_tsne, columns = ['Component 1', 'Component 2'])


mkt_tsne = tsne.fit_transform(data_scaled)

# Converting the embeddings to a dataframe
mkt_tsne = pd.DataFrame(mkt_tsne, columns = ["Component 1", "Component 2"])

# Scatter plot for two components
plt.figure(figsize = (7, 7))

sns.scatterplot(x = data_tsne.iloc[:,0], y = data_tsne.iloc[:,1])

plt.show()


plt.figure(figsize = (16, 16))

sns.heatmap(data_model.corr(), annot = True, fmt = '0.2f') # Shows correlation between variables.

plt.show()


# Importing PCA and t-SNE
from sklearn.decomposition import PCA

# PCA helps with dimensionality reduction.
# Defining the number of principal components to generate 
n = data_scaled.shape[1]

# Finding principal components for the data
# Apply the PCA algorithm with random_state = 1
pca = PCA(n_components=n, random_state=1) # Complete the code 

# Fit and transform the pca function on scaled data
data_pca1 = pd.DataFrame(pca.fit_transform(data_scaled)) # Complete the code    

# The percentage of variance explained by each principal component
exp_var = pca.explained_variance_ratio_

data_pca_copy = pd.DataFrame(data_pca1, columns = df.columns)


exp_var

array([4.48668019e-01, 1.16533332e-01, 6.94607714e-02, 6.02872096e-02,
       5.78382351e-02, 4.16408241e-02, 3.79264953e-02, 3.54636976e-02,
       2.76229835e-02, 2.52984813e-02, 2.31137962e-02, 2.30471218e-02,
       1.58105413e-02, 1.44767616e-02, 2.81172999e-03, 6.80243099e-33,
       1.75106629e-33])


# Visualize the explained variance by individual components
plt.figure(figsize = (10, 10))

plt.plot(range(1, 18), exp_var.cumsum(), marker = 'o', linestyle = '--')

plt.title("Explained Variances by Components")

plt.xlabel("Number of Components")

plt.ylabel("Cumulative Explained Variance")

plt.show()


np.cumsum(exp_var)

array([0.44866802, 0.56520135, 0.63466212, 0.69494933, 0.75278757,
       0.79442839, 0.83235489, 0.86781858, 0.89544157, 0.92074005,
       0.94385385, 0.96690097, 0.98271151, 0.99718827, 1.        ,
       1.        , 1.        ])


pc_comps = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6']

data_pca = pd.DataFrame(np.round(pca.components_[:6,:], 2), index = pc_comps, columns = data_scaled.columns) # Visialize PC coefficients.


def color_high(val):
    if val <= -0.40:
        return 'background: pink'
    
    elif val >= 0.40:
        return 'background: skyblue'   
    
data_pca.T.style.applymap(color_high)


# df_model.head()


# Importing clustering algorithms
from sklearn.cluster import KMeans

#  K-Means is a distance-based clustering algorithm that aims to minimize the within-cluster sum of squares
# step 1
sse = {} 

# step 2 - iterate for a range of Ks and fit the pca components to the algorithm. 
for k in range(1, 10):
    kmeans = KMeans(n_clusters = k, max_iter = 1000, random_state = 1).fit(data_pca1)
    sse[k] = kmeans.inertia_     # Use inertia attribute from the clustering object and store the inertia value for that K

# step 3
plt.figure()

plt.plot(list(sse.keys()), list(sse.values()), 'bx-')

plt.xlabel("Number of cluster")

plt.ylabel("SSE")

plt.show()


sil_score = []                                                             # Creating empty list
cluster_list = range(3, 7)                                                 # Creating a range from 3 to 7
for n_clusters in cluster_list:
    
    # Initialize K-Means with number of clusters equal to n_clusters and random_state=1
    clusterer = KMeans(n_clusters=n_clusters, random_state=1)
    
    # Fit and predict on the pca data
    preds = clusterer.fit_predict(data_pca1)
    
    # Calculate silhouette score - Hint: Use silhouette_score() function
    score = silhouette_score(data_pca1, preds)
    
    # Append silhouette score to empty list created above
    sil_score.append(score)
    
    # Print the silhouette score
    print("For n_clusters = {}, the silhouette score is {}".format(n_clusters, score))

For n_clusters = 3, the silhouette score is 0.27201384838171144
For n_clusters = 4, the silhouette score is 0.25661259468245723
For n_clusters = 5, the silhouette score is 0.23583061465612465
For n_clusters = 6, the silhouette score is 0.13010333058878457


kmeans = KMeans(n_clusters = 3, random_state = 1)

kmeans.fit(data_pca1)

KMeans(n_clusters=3, random_state=1)

KMeans(n_clusters=3, random_state=1)


data_pca1["K_means_segments_3"] = kmeans.labels_                    # Adding K-Means cluster labels to the data_pca data

df["K_means_segments_3"] = kmeans.labels_                        # Adding K-Means cluster labels to the whole data

data_scaled["K_means_segments_3"] = kmeans.labels_                  # Adding K-Means cluster labels to data_scaled


silhouette_avg = silhouette_score(data_pca1, kmeans.labels_)
print("The silhouette score for data_pca1 is:", silhouette_avg)

The silhouette score for data_pca1 is: 0.29586573188903226


# Df stores methods and silhouettte score
results = []

# Append method and score to the results list
results.append({"Method": 'Kmeans_3', "Score": silhouette_avg})


print(results)

[{'Method': 'Kmeans_3', 'Score': 0.29586573188903226}]


# Function to visualize PCA data with clusters formed
def PCA_PLOT(X, Y, PCA, cluster):
    sns.scatterplot(x=X, y=1, data=PCA, hue=cluster)


PCA_PLOT(0, 1, data_pca1, "K_means_segments_3")


cluster_profile_KMeans_3 = data_pca1.groupby('K_means_segments_3').mean()
cluster_profile_KMeans_3.style.highlight_max(color="lightgreen", axis=0)


# Columns to use in boxplot
# col_for_box = ['Income','Kidhome','Teenhome','Recency','MntWines','MntFruits','MntMeatProducts','MntFishProducts','MntSweetProducts','MntGoldProds','NumDealsPurchases','NumWebPurchases','NumCatalogPurchases','NumStorePurchases','NumWebVisitsMonth','Complain','Age','Family_Size','Expenses','NumTotalPurchases','Engaged_in_days','TotalAcceptedCmp','AmountPerPurchase']


# Creating boxplot for each of the variables
all_col = ['Income','NumDealsPurchases','NumWebVisitsMonth', 'NumWebPurchases','TotalAcceptedCmp', 'Expenses','NumTotalPurchases']

plt.figure(figsize = (20, 50))

for i, variable in enumerate(all_col):
    plt.subplot(6, 4, i + 1)
    
    sns.boxplot(y=df[variable], x=df['K_means_segments_3'],showmeans=True)
    
    plt.tight_layout()
    
    plt.title(variable)

plt.show()


# Drop columns
data_pca1.drop("K_means_segments_3", axis=1, inplace=True)
df.drop("K_means_segments_3", axis=1, inplace=True)


kmeans = KMeans(n_clusters = 5, random_state = 1)

kmeans.fit(data_pca1)

KMeans(n_clusters=5, random_state=1)

KMeans(n_clusters=5, random_state=1)


data_pca1["K_means_segments_5"] = kmeans.labels_  # Adding K-Means cluster labels to the data_pca data

df["K_means_segments_5"] = kmeans.labels_     # Adding K-Means cluster labels to the whole data

data_scaled["K_means_segments_5"] = kmeans.labels_  # Adding K-Means cluster labels to data_scaled


silhouette_avg = silhouette_score(data_pca1, kmeans.labels_)
print("The silhouette score for data_pca1 is:", silhouette_avg)

The silhouette score for data_pca1 is: 0.2894335595121166


# Append method and score to the results list
results.append({"Method": 'Kmeans_5', "Score": silhouette_avg})
print(results)

[{'Method': 'Kmeans_3', 'Score': 0.29586573188903226}, {'Method': 'Kmeans_5', 'Score': 0.2894335595121166}]


PCA_PLOT(0, 1, data_pca1, "K_means_segments_5")


cluster_profile_KMeans_5 = data_pca1.groupby('K_means_segments_5').mean()
cluster_profile_KMeans_5.style.highlight_max(color="lightgreen", axis=0)


# Creating boxplot for each of the variables

plt.figure(figsize = (20, 50))

for i, variable in enumerate(all_col):
    plt.subplot(6, 4, i + 1)
    
    sns.boxplot(y=df[variable], x=df['K_means_segments_5'],showmeans=True)
    
    plt.tight_layout()
    
    plt.title(variable)

plt.show()


from sklearn_extra.cluster import KMedoids

# Creating copy of the data to store labels from each algorithm
# data_scaled_copy = data_k_pca.copy(deep = True)

for n_clusters in range(2, 5):
    # Initialize K-Medoids with the specified number of clusters
    kmedo = KMedoids(n_clusters=n_clusters, random_state=1)
    kmedo.fit(data_pca1)

    # Predict cluster labels for the data points
    df[f'kmedoLabels_{n_clusters}'] = kmedo.predict(data_pca1)

    # Calculate silhouette score
    silhouette_avg = silhouette_score(data_pca1, kmedo.labels_)
    results.append({"Method": f'Medioids_{n_clusters}', "Score": silhouette_avg})
    print(f"The silhouette score for data_pca1 with {n_clusters} clusters is: {silhouette_avg}")

The silhouette score for data_pca1 with 2 clusters is: 0.3127386542698528
The silhouette score for data_pca1 with 3 clusters is: 0.12521679142543043
The silhouette score for data_pca1 with 4 clusters is: 0.11421820199844311


# Print silhouette scores
print(results)

[{'Method': 'Kmeans_3', 'Score': 0.29586573188903226}, {'Method': 'Kmeans_5', 'Score': 0.2894335595121166}, {'Method': 'Medioids_2', 'Score': 0.3127386542698528}, {'Method': 'Medioids_3', 'Score': 0.12521679142543043}, {'Method': 'Medioids_4', 'Score': 0.11421820199844311}]


plt.figure(figsize=(20, 20))


for i, col in enumerate(all_col):
    plt.subplot(6, 4, i + 1)
    sns.boxplot(x='kmedoLabels_2', y=col, data=df)
    plt.tight_layout()
    
    plt.title(col)

plt.show()


plt.figure(figsize=(20, 20))


for i, col in enumerate(all_col):
    plt.subplot(6, 4, i + 1)
    sns.boxplot(x='kmedoLabels_3', y=col, data=df)
    plt.tight_layout()
    
    plt.title(col)

plt.show()


# Drop columns
data_pca1.drop("K_means_segments_5", axis=1, inplace=True)
df.drop("K_means_segments_5", axis=1, inplace=True)


# list of distance metrics
distance_metrics = ["euclidean", "chebyshev", "mahalanobis", "cityblock"]

# list of linkage methods
linkage_methods = ["single", "complete", "average"]

high_cophenet_corr = 0                                                 # Creating a variable by assigning 0 to it
high_dm_lm = [0, 0]                                                    # Creating a list by assigning 0's to it

for dm in distance_metrics:
    for lm in linkage_methods:
        Z = linkage(data_pca1, metric=dm, method=lm)                    # Applying different linkages with different distance on data_pca
        c, coph_dists = cophenet(Z, pdist(data_pca1))                   # Calculating cophenetic correlation
        print(
            "Cophenetic correlation for {} distance and {} linkage is {}.".format(
                dm.capitalize(), lm, c
            )
        )
        if high_cophenet_corr < c:                                     # Checking if cophenetic correlation is higher than previous score
            high_cophenet_corr = c                                     # Appending to high_cophenet_corr list if it is higher
            high_dm_lm[0] = dm                                         # Appending its corresponding distance
            high_dm_lm[1] = lm                                         # Appending its corresponding method or linkage

Cophenetic correlation for Euclidean distance and single linkage is 0.8129807227587553.
Cophenetic correlation for Euclidean distance and complete linkage is 0.7537524321636122.
Cophenetic correlation for Euclidean distance and average linkage is 0.8151761018960105.
Cophenetic correlation for Chebyshev distance and single linkage is 0.8051749754976828.
Cophenetic correlation for Chebyshev distance and complete linkage is 0.6813736478845351.
Cophenetic correlation for Chebyshev distance and average linkage is 0.774784181804776.
Cophenetic correlation for Mahalanobis distance and single linkage is 0.5376203582123958.
Cophenetic correlation for Mahalanobis distance and complete linkage is 0.3693232898614882.
Cophenetic correlation for Mahalanobis distance and average linkage is 0.5881693054344688.
Cophenetic correlation for Cityblock distance and single linkage is 0.8149845035005294.
Cophenetic correlation for Cityblock distance and complete linkage is 0.7627696622102742.
Cophenetic correlation for Cityblock distance and average linkage is 0.8411010987060117.


# Printing the combination of distance metric and linkage method with the highest cophenetic correlation
print(
    "Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.".format(
        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]
    )
)

Highest cophenetic correlation is 0.8411010987060117, which is obtained with Cityblock distance and average linkage.


# List of linkage methods
linkage_methods = ["single", "complete", "average"]

# Lists to save results of cophenetic correlation calculation
compare_cols = ["Linkage", "Cophenetic Coefficient"]

# To create a subplot image
fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))            # Setting the plot size

# We will enumerate through the list of linkage methods above
# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation
for i, method in enumerate(linkage_methods):
    Z = linkage(data_pca1, metric="Cityblock", method=method)                  # Measures the distances between two clusters

    dendrogram(Z, ax=axs[i])
    axs[i].set_title(f"Dendrogram ({method.capitalize()} Linkage)")           # Title of dendrogram

    coph_corr, coph_dist = cophenet(Z, pdist(data_pca1))                       # Finding cophenetic correlation for different linkages with city block distance
    axs[i].annotate(
        f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
        (0.80, 0.80),
        xycoords="axes fraction",
    )


# List of linkage methods
linkage_methods = ["single", "complete", "average"]

# Lists to save results of cophenetic correlation calculation
compare_cols = ["Linkage", "Cophenetic Coefficient"]

# To create a subplot image
fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))  # Setting the plot size

# We will enumerate through the list of linkage methods above
# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation
for i, method in enumerate(linkage_methods):
    Z = linkage(data_pca1, metric="chebyshev", method=method)  # Measures the distances between two clusters

    dendrogram(Z, ax=axs[i])
    axs[i].set_title(f"Dendrogram ({method.capitalize()} Linkage)")  # Title of dendrogram0
    coph_corr, coph_dist = cophenet(Z, pdist(data_pca1))  # Finding cophenetic correlation for different linkages with Chebyshev distance
    axs[i].annotate(
        f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
        (0.80, 0.80),
        xycoords="axes fraction",
    )

# Show the plot
plt.tight_layout()
plt.show()


plt.figure(figsize = (20, 7))  

plt.title("Dendrograms")  

dend = dendrogram(linkage(data_scaled, method = 'complete'))

plt.axhline(y = 15, color = 'r', linestyle = '--')

<matplotlib.lines.Line2D at 0x15a50e09970>


# data_pca1 = data_pca1.drop('dendrogram', axis=1)

# data_pca1.head()


HCmodel = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='ward')
HCmodel.fit(data_pca1)

AgglomerativeClustering(affinity='euclidean', n_clusters=4)

AgglomerativeClustering(affinity='euclidean', n_clusters=4)


data_pca1["dendrogram"] = HCmodel.labels_  # Adding K-Means cluster labels to the data_pca data

df["dendrogram"] = HCmodel.labels_     # Adding K-Means cluster labels to the whole data

data_scaled["dendrogram"] = HCmodel.labels_  # Adding K-Means cluster labels to data_scaled


silhouette_avg = silhouette_score(data_pca1, HCmodel.labels_)
print("The silhouette score for data_pca1 is:", silhouette_avg)

The silhouette score for data_pca1 is: 0.22837691444799138


# Append method and score to the results list
results.append({"Method": 'Dendrogram', "Score": silhouette_avg})
print(results)

[{'Method': 'Kmeans_3', 'Score': 0.29586573188903226}, {'Method': 'Kmeans_5', 'Score': 0.2894335595121166}, {'Method': 'Medioids_2', 'Score': 0.3127386542698528}, {'Method': 'Medioids_3', 'Score': 0.12521679142543043}, {'Method': 'Medioids_4', 'Score': 0.11421820199844311}, {'Method': 'Dendrogram', 'Score': 0.22837691444799138}]


PCA_PLOT(0, 1, data_pca1, "dendrogram")


# Creating boxplot for each of the variables

plt.figure(figsize = (20, 50))

for i, variable in enumerate(all_col):
    plt.subplot(6, 4, i + 1)
    
    sns.boxplot(y=df[variable], x=df['dendrogram'],showmeans=True)
    
    plt.tight_layout()
    
    plt.title(variable)

plt.show()


df.dendrogram.value_counts()

1    928
0    591
2    450
3    239
Name: dendrogram, dtype: int64


data_pca1.drop("dendrogram", axis=1, inplace=True)


# Initializing lists
eps_value = [2,3]                       # Taking random eps value
min_sample_values = [6,20]              # Taking random min_sample value

# Creating a dictionary for each of the values in eps_value with min_sample_values
res = {eps_value[i]: min_sample_values for i in range(len(eps_value))}


from sklearn.cluster import DBSCAN


# Finding the silhouette_score for each of the combinations
high_silhouette_avg = 0                                               # Assigning 0 to the high_silhouette_avg variable
high_i_j = [0, 0]                                                     # Assigning 0's to the high_i_j list
key = res.keys()                                                      # Assigning dictionary keys to a variable called key
for i in key:
    z = res[i]                                                        # Assigning dictionary values of each i to z
    for j in z:
        db = DBSCAN(eps=i, min_samples=j).fit(data_pca1)               # Applying DBSCAN to each of the combination in dictionary
        core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
        core_samples_mask[db.core_sample_indices_] = True
        labels = db.labels_
        silhouette_avg = silhouette_score(data_pca1, labels)           # Finding silhouette score 
        print( 
            "For eps value =" + str(i),
            "For min sample =" + str(j),
            "The average silhoutte_score is :",
            silhouette_avg,                                          # Printing the silhouette score for each of the combinations
        )
        if high_silhouette_avg < silhouette_avg:                     # If the silhouette score is greater than 0 or the previous score, it will get appended to the high_silhouette_avg list with its combination of i and j              
            high_i_j[0] = i
            high_i_j[1] = j
            high_silhouette_avg = silhouette_avg

For eps value =2 For min sample =6 The average silhoutte_score is : 0.07631293391537533
For eps value =2 For min sample =20 The average silhoutte_score is : 0.3445089447463355
For eps value =3 For min sample =6 The average silhoutte_score is : 0.3307327349278307
For eps value =3 For min sample =20 The average silhoutte_score is : 0.34151752330706703


# Printing the highest silhouette score
print("Highest silhouette average is {:.2f} for eps = {} and min_samples = {}".format(high_silhouette_avg, high_i_j[0], high_i_j[1]))

Highest silhouette average is 0.34 for eps = 2 and min_samples = 20


# Append method and score to the results list
results.append({"Method": 'DBSCAN', "Score": high_silhouette_avg})
print(results)

[{'Method': 'Kmeans_3', 'Score': 0.29586573188903226}, {'Method': 'Kmeans_5', 'Score': 0.2894335595121166}, {'Method': 'Medioids_2', 'Score': 0.3127386542698528}, {'Method': 'Medioids_3', 'Score': 0.12521679142543043}, {'Method': 'Medioids_4', 'Score': 0.11421820199844311}, {'Method': 'Dendrogram', 'Score': 0.22837691444799138}, {'Method': 'DBSCAN', 'Score': 0.3445089447463355}]


dbs = DBSCAN(eps=3, min_samples=20)


data_pca1["DBSCAN_cluster"] = kmeans.labels_                    # Adding DBSCAN cluster labels to the data_pca data

df["DBSCAN_cluster"] = kmeans.labels_                        # Adding DBSCAN cluster labels to the whole data

data_scaled["DBSCAN_cluster"] = kmeans.labels_                  # Adding DBSCAN cluster labels to data_scaled


PCA_PLOT(0, 1, data_pca1, "DBSCAN_cluster")


# Creating boxplot for each of the variables
# all_col = ['Income','Teenhome', 'Kidhome', 'Age', 'Family_Size', 'Expenses', 'NumTotalPurchases', 'Engaged_in_days', 'TotalAcceptedCmp', 'AmountPerPurchase']

plt.figure(figsize = (20, 50))

for i, variable in enumerate(all_col):
    plt.subplot(6, 4, i + 1)
    
    sns.boxplot(y=df[variable], x=df['DBSCAN_cluster'],showmeans=True)
    
    plt.tight_layout()
    
    plt.title(variable)

plt.show()


data_pca1 = data_pca1.drop('DBSCAN_cluster', axis=1)


# data_pca1 = data_pca1.drop('GMM_cluster', axis=1)


from sklearn.mixture import GaussianMixture

for n_components in range(3, 7):
    gmm = GaussianMixture(n_components=n_components, random_state=1)
    preds = gmm.fit_predict(data_pca1)
    score = silhouette_score(data_pca1, preds)
    
    print(f"Number of Components: {n_components}")
    print(f"Silhouette Score: {score}")
    
    results.append({"Method": f"GMM_{n_components}", "Score": score})

Number of Components: 3
Silhouette Score: 0.16256857703931887
Number of Components: 4
Silhouette Score: 0.15384146936782728
Number of Components: 5
Silhouette Score: 0.12086634023827815
Number of Components: 6
Silhouette Score: 0.06504147539555859


# Append method and score to the results list
results.append({"Method": 'GMM', "Score": score})
print(results)

[{'Method': 'Kmeans_3', 'Score': 0.29586573188903226}, {'Method': 'Kmeans_5', 'Score': 0.2894335595121166}, {'Method': 'Medioids_2', 'Score': 0.3127386542698528}, {'Method': 'Medioids_3', 'Score': 0.12521679142543043}, {'Method': 'Medioids_4', 'Score': 0.11421820199844311}, {'Method': 'Dendrogram', 'Score': 0.22837691444799138}, {'Method': 'DBSCAN', 'Score': 0.3445089447463355}, {'Method': 'GMM_3', 'Score': 0.16256857703931887}, {'Method': 'GMM_4', 'Score': 0.15384146936782728}, {'Method': 'GMM_5', 'Score': 0.12086634023827815}, {'Method': 'GMM_6', 'Score': 0.06504147539555859}, {'Method': 'GMM', 'Score': 0.06504147539555859}]


gmm = GaussianMixture(n_components=4, random_state=1)
preds = gmm.fit_predict(data_pca1)


data_pca1["GMM_cluster"] = preds                    # Adding GMM cluster labels to the data_pca data

df["GMM_cluster"] = preds                        # Adding GMM cluster labels to the whole data
# 
data_scaled["GMM_cluster"] = preds                  # Adding GMM cluster labels to data_scaled


df["GMM_cluster"].value_counts()

0    750
2    601
1    443
3    414
Name: GMM_cluster, dtype: int64


PCA_PLOT(0, 1, data_pca1, "GMM_cluster")


# Creating boxplot for each of the variables

plt.figure(figsize = (20, 50))

for i, variable in enumerate(all_col):
    plt.subplot(6, 4, i + 1)
    
    sns.boxplot(y=df[variable], x=df['GMM_cluster'],showmeans=True)
    
    plt.tight_layout()
    
    plt.title(variable)

plt.show()


# Convert the results list to a DataFrame
results_df = pd.DataFrame(results)
styled_results = results_df.style.background_gradient(subset=["Score"], cmap="Greens")

# Print the results DataFrame
display(styled_results)


prefered_model_stats = df.groupby('DBSCAN_cluster')['NumTotalPurchases'].count()

total_counts = prefered_model_stats.sum()
prefered_model_stats = prefered_model_stats.to_frame(name='CustomerCount')
prefered_model_stats['Percentage of Total Customers'] = prefered_model_stats['CustomerCount'] / total_counts * 100

formatted_prefered_model_stats = prefered_model_stats.round().astype(int)
formatted_prefered_model_stats["CustomerCount"] = formatted_prefered_model_stats["CustomerCount"]
formatted_prefered_model_stats["Percentage of Total Customers"] = formatted_prefered_model_stats["Percentage of Total Customers"]
styled_prefered_model_stats = formatted_prefered_model_stats.style.background_gradient(cmap="RdYlGn")
styled_prefered_model_stats.background_color = 'lightblue'

styled_prefered_model_stats.set_caption("Customer Count")


df.groupby('DBSCAN_cluster')['Expenses', 'NumTotalPurchases'].mean()


prefered_model_stats = df.groupby('DBSCAN_cluster')['Expenses', 'NumTotalPurchases'].sum()
prefered_model_stats['AmountPerPurchase'] = prefered_model_stats['Expenses'] / prefered_model_stats['NumTotalPurchases']

formatted_prefered_model_stats = prefered_model_stats.round().astype(int)
formatted_prefered_model_stats = formatted_prefered_model_stats.rename(columns={"Expenses": "Revenue", "NumTotalPurchases": "Total Purchases"})
formatted_prefered_model_stats["AmountPerPurchase"] = formatted_prefered_model_stats["AmountPerPurchase"]

# Calculate the percent of total for each column
total_revenue = formatted_prefered_model_stats["Revenue"].sum()
total_purchases = formatted_prefered_model_stats["Total Purchases"].sum()
formatted_prefered_model_stats["PercentRevenue"] = (formatted_prefered_model_stats["Revenue"] / total_revenue * 100).round().astype(int)
formatted_prefered_model_stats["PercentPurchases"] = (formatted_prefered_model_stats["Total Purchases"] / total_purchases * 100).round().astype(int)

styled_prefered_model_stats = formatted_prefered_model_stats.style.background_gradient(cmap="RdYlGn")
styled_prefered_model_stats.background_color = 'lightblue'

styled_prefered_model_stats.set_caption("Customer Total Sum")


prefered_model_stats_per_customer = df.groupby('DBSCAN_cluster')['Expenses', 'NumTotalPurchases'].mean()
prefered_model_stats_per_customer['AmountPerPurchase'] = prefered_model_stats['Expenses'] / prefered_model_stats['NumTotalPurchases']


formatted_prefered_model_stats_per_customer = prefered_model_stats_per_customer.round().astype(int)
formatted_prefered_model_stats_per_customer["Expenses"] = formatted_prefered_model_stats_per_customer["Expenses"]
formatted_prefered_model_stats_per_customer["NumTotalPurchases"] = formatted_prefered_model_stats_per_customer["NumTotalPurchases"]
styled_prefered_model_stats_per_customer = formatted_prefered_model_stats_per_customer.style.background_gradient(cmap="RdYlGn")
styled_prefered_model_stats_per_customer.background_color = 'lightblue'

styled_prefered_model_stats_per_customer.set_caption("Purchase Stats per Customer")

	ID	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Response
count	2240.000000	2240.000000	2216.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000
mean	5592.159821	1968.805804	52247.251354	0.444196	0.506250	49.109375	303.935714	26.302232	166.950000	37.525446	27.062946	44.021875	2.325000	4.084821	2.662054	5.790179	5.316518	0.072768	0.074554	0.072768	0.064286	0.012946	0.009375	0.149107
std	3246.662198	11.984069	25173.076661	0.538398	0.544538	28.962453	336.597393	39.773434	225.715373	54.628979	41.280498	52.167439	1.932238	2.778714	2.923101	3.250958	2.426645	0.259813	0.262728	0.259813	0.245316	0.113069	0.096391	0.356274
min	0.000000	1893.000000	1730.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	2828.250000	1959.000000	35303.000000	0.000000	0.000000	24.000000	23.750000	1.000000	16.000000	3.000000	1.000000	9.000000	1.000000	2.000000	0.000000	3.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	5458.500000	1970.000000	51381.500000	0.000000	0.000000	49.000000	173.500000	8.000000	67.000000	12.000000	8.000000	24.000000	2.000000	4.000000	2.000000	5.000000	6.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	8427.750000	1977.000000	68522.000000	1.000000	1.000000	74.000000	504.250000	33.000000	232.000000	50.000000	33.000000	56.000000	3.000000	6.000000	4.000000	8.000000	7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	11191.000000	1996.000000	666666.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	263.000000	362.000000	15.000000	27.000000	28.000000	13.000000	20.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

	count	mean	std	min	25%	50%	75%	max
ID	2216.0	5588.353339	3249.376275	0.0	2814.75	5458.5	8421.75	11191.0
Year_Birth	2216.0	1968.820397	11.985554	1893.0	1959.00	1970.0	1977.00	1996.0
Income	2216.0	52247.251354	25173.076661	1730.0	35303.00	51381.5	68522.00	666666.0
Kidhome	2216.0	0.441787	0.536896	0.0	0.00	0.0	1.00	2.0
Teenhome	2216.0	0.505415	0.544181	0.0	0.00	0.0	1.00	2.0
Recency	2216.0	49.012635	28.948352	0.0	24.00	49.0	74.00	99.0
MntWines	2216.0	305.091606	337.327920	0.0	24.00	174.5	505.00	1493.0
MntFruits	2216.0	26.356047	39.793917	0.0	2.00	8.0	33.00	199.0
MntMeatProducts	2216.0	166.995939	224.283273	0.0	16.00	68.0	232.25	1725.0
MntFishProducts	2216.0	37.637635	54.752082	0.0	3.00	12.0	50.00	259.0
MntSweetProducts	2216.0	27.028881	41.072046	0.0	1.00	8.0	33.00	262.0
MntGoldProds	2216.0	43.965253	51.815414	0.0	9.00	24.5	56.00	321.0
NumDealsPurchases	2216.0	2.323556	1.923716	0.0	1.00	2.0	3.00	15.0
NumWebPurchases	2216.0	4.085289	2.740951	0.0	2.00	4.0	6.00	27.0
NumCatalogPurchases	2216.0	2.671029	2.926734	0.0	0.00	2.0	4.00	28.0
NumStorePurchases	2216.0	5.800993	3.250785	0.0	3.00	5.0	8.00	13.0
NumWebVisitsMonth	2216.0	5.319043	2.425359	0.0	3.00	6.0	7.00	20.0
AcceptedCmp3	2216.0	0.073556	0.261106	0.0	0.00	0.0	0.00	1.0
AcceptedCmp4	2216.0	0.074007	0.261842	0.0	0.00	0.0	0.00	1.0
AcceptedCmp5	2216.0	0.073105	0.260367	0.0	0.00	0.0	0.00	1.0
AcceptedCmp1	2216.0	0.064079	0.244950	0.0	0.00	0.0	0.00	1.0
AcceptedCmp2	2216.0	0.013087	0.113672	0.0	0.00	0.0	0.00	1.0
Complain	2216.0	0.009477	0.096907	0.0	0.00	0.0	0.00	1.0
Response	2216.0	0.150271	0.357417	0.0	0.00	0.0	0.00	1.0

	ID	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Response	Age	Kids	Status	Family_Size	Expenses	NumTotalPurchases	Engaged_in_days	TotalAcceptedCmp	AmountPerPurchase
0	-0.017526	-1.018743	0.292925	-0.823235	-0.931270	0.311060	0.975863	1.550053	1.688262	2.451041	1.481762	0.850833	0.348987	1.425749	2.501580	-0.560167	0.698394	-0.282324	-0.283258	-0.280449	-0.262168	-0.115364	-0.095607	2.372895	1.018743	-1.266516	-1.351719	-1.761762	1.674434	1.317746	1.526655	0.617845	1.120445
1	-1.049811	-1.275141	-0.261644	1.039166	0.906303	-0.380080	-0.874099	-0.638273	-0.719275	-0.651903	-0.634855	-0.734941	-0.170961	-1.130379	-0.572908	-1.176741	-0.132230	-0.282324	-0.283258	-0.280449	-0.262168	-0.115364	-0.095607	-0.421426	1.275141	1.403143	-1.351719	0.447193	-0.965102	-1.165726	-1.190250	-0.504032	-0.986284
2	-0.443690	-0.335013	0.926537	-0.823235	-0.931270	-0.794765	0.356244	0.569079	-0.179808	1.337632	-0.148276	-0.038747	-0.690910	1.425749	-0.231298	1.289556	-0.547542	-0.282324	-0.283258	-0.280449	-0.262168	-0.115364	-0.095607	-0.421426	0.335013	-1.266516	0.739799	-0.657284	0.278302	0.794910	-0.207224	-0.504032	0.149781
3	0.185233	1.288844	-1.187869	1.039166	-0.931270	-0.794765	-0.874099	-0.562814	-0.656857	-0.505882	-0.586197	-0.754280	-0.170961	-0.765218	-0.914518	-0.560167	0.283082	-0.282324	-0.283258	-0.280449	-0.262168	-0.115364	-0.095607	-0.421426	-1.288844	0.068313	0.739799	0.447193	-0.921940	-0.904308	-1.061814	-0.504032	-0.911894
4	-0.079155	1.032445	0.300213	1.039166	-0.931270	1.555113	-0.393821	0.418160	-0.219934	0.151212	-0.002303	-0.560893	1.388885	0.330266	0.110311	0.056408	-0.132230	-0.282324	-0.283258	-0.280449	-0.262168	-0.115364	-0.095607	-0.421426	-1.032445	0.068313	0.739799	0.447193	-0.309368	0.533492	-0.953138	-0.504032	-0.366289

	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Expenses	NumTotalPurchases	Engaged_in_days	TotalAcceptedCmp	AmountPerPurchase
0	0.311060	0.975863	1.550053	1.688262	2.451041	1.481762	0.850833	0.348987	1.425749	2.501580	-0.560167	0.698394	1.674434	1.317746	1.526655	0.617845	1.120445
1	-0.380080	-0.874099	-0.638273	-0.719275	-0.651903	-0.634855	-0.734941	-0.170961	-1.130379	-0.572908	-1.176741	-0.132230	-0.965102	-1.165726	-1.190250	-0.504032	-0.986284
2	-0.794765	0.356244	0.569079	-0.179808	1.337632	-0.148276	-0.038747	-0.690910	1.425749	-0.231298	1.289556	-0.547542	0.278302	0.794910	-0.207224	-0.504032	0.149781
3	-0.794765	-0.874099	-0.562814	-0.656857	-0.505882	-0.586197	-0.754280	-0.170961	-0.765218	-0.914518	-0.560167	0.283082	-0.921940	-0.904308	-1.061814	-0.504032	-0.911894
4	1.555113	-0.393821	0.418160	-0.219934	0.151212	-0.002303	-0.560893	1.388885	0.330266	0.110311	0.056408	-0.132230	-0.309368	0.533492	-0.953138	-0.504032	-0.366289

	PC1	PC2	PC3	PC4	PC5	PC6
Recency	0.010000	0.000000	-0.320000	0.540000	-0.750000	-0.120000
MntWines	0.300000	0.130000	0.290000	-0.050000	-0.220000	0.030000
MntFruits	0.250000	-0.150000	-0.230000	0.120000	0.280000	-0.060000
MntMeatProducts	0.300000	-0.150000	0.060000	0.080000	-0.040000	0.330000
MntFishProducts	0.260000	-0.160000	-0.220000	0.140000	0.260000	-0.050000
MntSweetProducts	0.250000	-0.130000	-0.220000	0.130000	0.220000	-0.020000
MntGoldProds	0.210000	0.100000	-0.110000	0.120000	0.160000	-0.710000
NumDealsPurchases	-0.020000	0.550000	-0.170000	-0.140000	-0.020000	0.200000
NumWebPurchases	0.210000	0.380000	-0.060000	-0.170000	-0.030000	-0.300000
NumCatalogPurchases	0.300000	-0.020000	0.020000	-0.050000	-0.110000	0.210000
NumStorePurchases	0.270000	0.130000	-0.190000	-0.260000	-0.080000	0.100000
NumWebVisitsMonth	-0.200000	0.410000	0.170000	0.250000	0.140000	-0.120000
Expenses	0.350000	-0.010000	0.130000	0.040000	-0.070000	0.070000
NumTotalPurchases	0.300000	0.320000	-0.140000	-0.220000	-0.090000	0.070000
Engaged_in_days	0.060000	0.370000	0.100000	0.610000	0.330000	0.330000
TotalAcceptedCmp	0.150000	-0.000000	0.680000	0.050000	-0.090000	-0.230000
AmountPerPurchase	0.320000	-0.120000	0.200000	0.140000	-0.030000	0.010000

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Response
column type	int64	int64	object	object	float64	int64	int64	object	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64
null values (nb)	0	0	0	0	24	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
null values (%)	0.0	0.0	0.0	0.0	1.071429	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Response
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	88	546	172	88	88	3	8	10	4	7	1
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	1	6	2	1	6	2	1	1	2	5	0
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	49	127	111	21	42	1	8	2	10	4	0
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	4	20	10	3	5	2	2	0	4	6	0
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	43	118	46	27	15	5	5	3	6	5	0

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	Response
10	1994	1983	Graduation	Married	NaN	1	0	2013-11-15	11	5	5	6	0	2	1	1	1	0	2	7	0	0	0	0
27	5255	1986	Graduation	Single	NaN	1	0	2013-02-20	19	5	1	3	3	263	362	0	27	0	0	1	0	0	0	0
43	7281	1959	PhD	Single	NaN	0	0	2013-11-05	80	81	11	50	3	2	39	1	1	3	4	2	0	0	0	0
48	7244	1951	Graduation	Single	NaN	2	1	2014-01-01	96	48	5	48	6	10	7	3	2	1	4	6	0	0	0	0
58	8557	1982	Graduation	Single	NaN	1	0	2013-06-17	57	11	3	22	2	2	6	2	2	0	3	6	0	0	0	0
71	10629	1973	2n Cycle	Married	NaN	1	0	2012-09-14	25	25	3	43	17	4	17	3	3	0	3	8	0	0	0	0
90	8996	1957	PhD	Married	NaN	2	1	2012-11-19	4	230	42	192	49	37	53	12	7	2	8	9	0	0	0	0
91	9235	1957	Graduation	Single	NaN	1	1	2014-05-27	45	7	0	8	2	0	1	1	1	0	2	7	0	0	0	0
92	5798	1973	Master	Together	NaN	0	0	2013-11-23	87	445	37	359	98	28	18	1	2	4	8	1	0	0	0	0
128	8268	1961	PhD	Married	NaN	0	1	2013-07-11	23	352	0	27	10	0	15	3	6	1	7	6	0	0	0	0
133	1295	1963	Graduation	Married	NaN	0	1	2013-08-11	96	231	65	196	38	71	124	1	6	5	7	4	0	0	0	0
312	2437	1989	Graduation	Married	NaN	0	0	2013-06-03	69	861	138	461	60	30	61	1	6	5	12	3	1	0	1	0
319	2863	1970	Graduation	Single	NaN	1	2	2013-08-23	67	738	20	172	52	50	20	6	2	3	10	7	1	0	1	0
1379	10475	1970	Master	Together	NaN	0	1	2013-04-01	39	187	5	65	26	20	14	2	4	2	6	5	0	0	0	0
1382	2902	1958	Graduation	Together	NaN	1	1	2012-09-03	87	19	4	12	2	2	6	1	1	0	3	5	0	0	0	0
1383	4345	1964	2n Cycle	Single	NaN	1	1	2014-01-12	49	5	1	9	2	0	4	1	1	0	2	7	0	0	0	0
1386	3769	1972	PhD	Together	NaN	1	0	2014-03-02	17	25	1	13	0	0	3	1	1	0	3	7	0	0	0	0
2059	7187	1969	Master	Together	NaN	1	1	2013-05-18	52	375	42	48	94	66	96	7	4	10	4	3	0	0	0	0
2061	1612	1981	PhD	Single	NaN	1	0	2013-05-31	82	23	0	15	0	2	7	2	3	0	3	6	0	0	0	0
2078	5079	1971	Graduation	Married	NaN	1	1	2013-03-03	82	71	1	16	0	0	9	4	2	1	3	8	0	0	0	0
2079	10339	1954	Master	Together	NaN	0	1	2013-06-23	83	161	0	22	0	0	24	2	4	1	4	6	0	0	0	0
2081	3117	1955	Graduation	Single	NaN	0	1	2013-10-18	95	264	0	21	12	6	147	3	6	1	5	7	0	0	0	0
2084	5250	1943	Master	Widow	NaN	0	0	2013-10-30	75	532	126	490	164	126	126	1	5	5	11	1	0	1	0	1
2228	8720	1978	2n Cycle	Together	NaN	0	0	2012-08-12	53	32	2	1607	12	4	22	0	0	0	1	0	1	0	0	0

	count	unique	top	freq
Education	2216	5	Graduation	1116
Marital_Status	2216	8	Married	857
Dt_Customer	2216	662	31-08-2012	12

	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Expenses	NumTotalPurchases	Engaged_in_days	TotalAcceptedCmp	AmountPerPurchase
0	58	635	88	546	172	88	88	3	8	10	4	7	1617	25	849	1	64.680000
1	38	11	1	6	2	1	6	2	1	1	2	5	27	6	299	0	4.500000
2	26	426	49	127	111	21	42	1	8	2	10	4	776	21	498	0	36.952381
3	26	11	4	20	10	3	5	2	2	0	4	6	53	8	325	0	6.625000
4	94	173	43	118	46	27	15	5	5	3	6	5	422	19	347	0	22.210526

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
K_means_segments_3
0	0.768588	1.374578	-0.211799	-0.431943	-0.153868	-0.107772	-0.153745	-0.156302	0.040737	-0.044713	-0.005825	-0.024341	0.061141	-0.020934	0.071660	0.000000	0.000000
1	-2.484489	-0.360640	0.076136	0.134909	0.050476	0.014695	0.036093	0.046186	0.005626	-0.002559	-0.000593	-0.000505	-0.026724	0.014590	-0.035199	0.000000	0.000000
2	3.772440	-0.846935	0.092243	0.225994	0.076007	0.091663	0.102623	0.086674	-0.055443	0.054129	0.007534	0.027816	-0.017803	-0.004023	-0.013654	0.000000	0.000000

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
K_means_segments_5
0	-2.552404	-0.478570	0.081441	0.135359	0.045405	0.002666	0.011847	0.021778	0.010197	0.000036	0.002878	-0.007709	-0.036259	0.013451	-0.046011	0.000000	0.000000
1	1.578934	0.442909	-0.504456	-0.600998	-0.192214	-0.053760	-0.457999	-0.406103	0.090076	-0.030772	0.089143	0.036990	-0.091341	-0.018903	0.081260	0.000000	0.000000
2	-0.285200	2.021514	-0.019120	-0.139724	-0.035601	-0.052136	0.290267	0.183086	-0.051196	-0.013738	-0.073046	-0.015239	0.215441	-0.020764	0.078775	0.000000	0.000000
3	4.155756	-0.297457	2.260443	0.280019	-0.626976	-0.155703	-0.144649	0.378139	0.248659	-0.151026	-0.074191	0.021742	0.078000	-0.032695	-0.060042	0.000000	0.000000
4	3.850381	-1.108559	-0.690979	0.342767	0.437119	0.186212	0.284502	0.054486	-0.204137	0.123750	-0.004193	-0.016664	-0.046377	0.020934	-0.017435	0.000000	0.000000

	Method	Score
0	Kmeans_3	0.295866
1	Kmeans_5	0.289434
2	Medioids_2	0.312739
3	Medioids_3	0.125217
4	Medioids_4	0.114218
5	Dendrogram	0.228377
6	DBSCAN	0.344509
7	GMM_3	0.162569
8	GMM_4	0.153841
9	GMM_5	0.120866
10	GMM_6	0.065041
11	GMM	0.065041

	Expenses	NumTotalPurchases
DBSCAN_cluster
0	87.796698	7.507740
1	867.887781	21.683292
2	518.135447	19.285303
3	1659.962264	21.301887
4	1404.900602	20.756024

	Revenue	Total Purchases	AmountPerPurchase	PercentRevenue	PercentPurchases
DBSCAN_cluster
0	85075	7275	12	6	22
1	348023	8695	40	26	26
2	179793	6692	27	13	20
3	263934	3387	78	20	10
4	466427	6891	68	35	21

Customer Count
	CustomerCount	Percentage of Total Customers
DBSCAN_cluster
0	969	44
1	401	18
2	347	16
3	159	7
4	332	15

Purchase Stats per Customer
	Expenses	NumTotalPurchases	AmountPerPurchase
DBSCAN_cluster
0	88	8	12
1	868	22	40
2	518	19	27
3	1660	21	78
4	1405	21	68

Marketing Campaign Analysis¶

Problem Definition¶

The Context:¶

The objective:¶

The key questions:¶

The problem formulation:¶

Data Dictionary¶

Import the necessary libraries and load the data¶

Data Overview¶

Observations and Insights from the Data overview: _¶

Exploratory Data Analysis (EDA)¶

Observation¶

Observation¶

Univariate Analysis on Numerical and Categorical data¶

Observation¶

Observation¶

Observation¶

Observation¶

Observation¶

Observation¶

Observation¶

Observations¶

Observations¶

Observation¶

Observation¶

Observations¶

Overall Observations:¶

Dimensionality Reduction and Clustering techniques¶

Value of Clusteing Techniques¶

Key Measures of success¶

Bivariate Analysis¶

Observation¶

Observation¶

Observation¶

Feature Engineering and Data Processing¶

Observation¶

Important Insights from EDA and Data Preprocessing¶

Data Preparation for Segmentation¶

Applying T-SNE and PCA to the data to visualize the data distributed in 2 dimensions¶

Applying T-SNE¶

Applying PCA¶

Observation¶

K-Means¶

Observation:¶

Applying KMeans on the PCA data and visualize the clusters¶

Observation:¶

Cluster Profiling¶

Observation¶

Describe the characteristics of each cluster¶

Observation¶

K-Medoids¶

Visualize the clusters using PCA¶

Cluster Profiling¶

Characteristics of each cluster¶

Hierarchical Clustering¶

Observation¶

Observation¶

Visualize the clusters using PCA¶

Cluster Profiling¶

Characteristics of each cluster¶

DBSCAN¶

Apply DBSCAN for the best hyperparameter and visualize the clusters from PCA¶

Observation:¶

Characteristics of each cluster¶

Gaussian Mixture Model¶

Visualize the clusters using PCA¶

Cluster Profiling¶

Characteristics of each cluster¶

Conclusion and Recommendations¶

Key Measures of success¶

Evaluate Clustering Techniques¶

Proposal for the Final Solution Design:¶

What are the diffrent customer segments and how much do they spend on average?¶

What are the key features that describe each segment?¶

Which segments should we focus on? Are there any we should avoid?¶

Executive Summary¶

Problem and Solution Summary¶

Recommendations for Implementation¶